Hasil untuk "Mechanical industries"

J. Rhim, P. Ng

J. García, F. García, F. Serna et al.

Congqi Yan, D. Pochan

P. Yvon, F. Carré

Xiaozhi Tang, S. Alavi

Ivo Beenakker, Linda Carton, Hans van Kranenburg et al.

Abstract Background In the Netherlands, a Regional Energy Strategy (RES) has been introduced to foster collaboration in allocating spatial and energy resources to meet climate goals. However, the market-based rules guiding the RES are often perceived as ineffective, unfair, and inefficient, resulting in limited participation and poor information sharing. Planning currently follows a ‘first come, first served’ principle. National legislative changes are being prepared to improve the RES. Yet, it remains unclear how these might improve the process. This study aims to explore, through a serious game, whether changes in rules can enhance the RES and, if so, how this improvement is achieved. Three scenarios are evaluated: (1) a business-as-usual situation with existing market rules; (2) a government-led scenario in which provinces play a stronger role by prioritizing spatial needs; and (3) a governance-led scenario in which energy planners are allowed to manage the grid more flexibly, giving grid planning a central role in steering spatial energy decisions. Results The findings show that altering the rules of the game allowed more flexible grid management, based on a governance rationale and collaborative decision-making. The changes in rules produced spatial energy planning outcomes perceived as much more effective, efficient, and fair. These simulated rule changes created better conditions for spatial and energy planners to interact and make decisions that optimize both spatial and energy needs. Conclusions The simulation results suggest that the RES can achieve better outcomes when specific rules are adjusted, particularly in relation to energy network planning, with key actors taking the lead under a governance-oriented approach.

K. N. Arun Kumar, G. V. Naveen Prakash, B. B. Ganesha et al.

Abstract The rapid evolution of 3D printing, also known as additive manufacturing, continues to transform various industries by enabling the fabrication of lightweight, complex, and customized structures. This study experimentally investigates the mechanical and thermal properties of 3D-printed sandwich panels using Polylactic Acid (PLA+), Acrylonitrile Butadiene Styrene (ABS+), and Enhanced Acrylonitrile Butadiene Styrene (e-ABS). Samples were prepared using FDM-based 3D printing with a honeycomb core structure and tested according to ASTM standards. Among the materials tested, PLA + demonstrated the highest compressive strength of 23.57 MPa, flexural strength of 39.63 MPa, and ultimate tensile strength (UTS) of 22.43 MPa, reflecting superior rigidity and load-bearing capacity. ABS + and e-ABS exhibited lower compressive strengths of 10.30 MPa and 10.07 MPa, and UTS values of 13.15 MPa and 12.33 MPa, respectively, with e-ABS offering slightly better flexibility due to a higher strain at break. Flexural modulus values also confirmed PLA + as the stiffest material at 3006.44 MPa, compared to 2093.84 MPa for ABS + and 1157.86 MPa for e-ABS. Thermal behaviour assessed via Differential Scanning Calorimetry (DSC) revealed glass transition temperatures of 66.95 °C (PLA+), 104.25 °C (ABS+), and 101.48 °C (e-ABS), indicating their suitability for varying thermal conditions. These results provide valuable insights into selecting appropriate 3D-printed thermoplastics for engineering applications requiring a balance of strength, stiffness, and thermal stability.

Negar Omidvar, Md Hafiz All Hosen, Michael B. Farrar et al.

ABSTRACT Optimising the efficiency of applied nitrogen (N) fertilisers is essential to sustain agricultural systems. Substantial N losses continue through leaching, volatilisation, and denitrification processes. Co‐application of organic amendments and biochar alongside synthetic fertilisers is a widely practiced strategy to enhance N retention, improve soil fertility and increase crop productivity. Previous studies have focused on the specific characteristics of soil amendments and the magnitude of yield change, while N use efficiency (NUE) and economic returns remain uncertain. This meta‐analysis examined the effects of synthetic fertiliser applied alone, co‐applied with biochar and co‐applied with organic amendments, on crop yield, plant N uptake, NUE and economic return within rice cropping systems. Synthetic fertiliser and biochar applied alone increased rice yield by 69.2% ± 30.3 and 33.4% ± 34.9, respectively (Bootstrap 95% CI), whereas yield further increased by co‐applying biochar (+104.8% ± 37.5) and organic amendments (+80.2% ± 18.2) with fertiliser compared with non‐fertilised control. Co‐applying organic amendments (+20.9% ± 29.7) and co‐applying biochar (+35.1% ± 18.3) with synthetic fertiliser increased NUE compared with fertilised control. For rice crops under low N application (< 150 kg ha−1), co‐applying biochar with fertiliser increased yields more than co‐applying organic amendments (+70.1% ± 0.7 vs. +52.5% ± 0.3, respectively). Within acidic soils, co‐applying biochar with fertiliser (+72.9% ± 0.4) led to higher yield than co‐applying organic matter (+36.0% ± 0.9), and among soils with high organic carbon concentration, co‐applying biochar with fertiliser increased yield by 97.6% ± 1.6, compared with yield increases observed by co‐applying organic matter with fertiliser at 29.4% ± 0.5 and fertiliser alone at 25.6% ± 0.2. The main factors driving rice yield were N application rate, co‐application method and soil organic carbon concentration. Co‐applying either biochar or organic amendments did not significantly differ in benefit–cost ratio with benefit–cost ratios of 35.1% ± 9.2 and 18.1 ± 26.5, respectively compared with fertilised control. Co‐applying either biochar or organic amendments with synthetic fertilisers decreased N inputs and increased economic return, therefore improving sustainability in rice cropping systems.

Dean Barr

Despite the scale of capital being deployed toward AI initiatives, no empirical framework currently exists for benchmarking where a firm stands relative to competitors in AI readiness and deployment, or for translating that position into auditable financial outcomes. In practice, private equity deal teams, management consultants, and corporate strategists have relied on qualitative judgment and ad-hoc maturity labels; tools that are neither comparable across industries nor grounded in observable economic data. This paper introduces the AI Transformation Gap Index (AITG), a composite empirical framework that measures the distance between a firm's current AI deployment and a time varying, industry constrained capability frontier, then maps that distance to dollar denominated value creation, execution feasibility under uncertainty, and competitive disruption risk. Five linked modules address this gap: cross industry normalization (IASS), a dynamic capability ceiling that evolves with frontier capabilities (AFC), trajectory based firm scoring with integrated execution risk (IFS), a CES bottleneck value decomposition mapping gap scores to enterprise value (VCB), and a competitive hazard measure for inaction (ADRI). I calibrate the framework for 22 industry verticals and apply it to 14 public companies using public filings. A retrospective construct validity exercise correlating AITG scores with observed EBITDA margin expansion yields Spearman rho_s = 0.818 (n = 10), directionally consistent with predictions though insufficient for causal identification. A counterintuitive result emerges: the largest AI transformation gaps do not produce the highest value density, because implementation friction, CES bottlenecks, and timing lags erode the theoretical upside of wide gaps.

Thanh Luong Tuan

Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Foundation AgenticOS (FAOS) platform that addresses these limitations through ontology-constrained neural reasoning. Our approach introduces a three-layer ontological framework--Role, Domain, and Interaction ontologies--that provides formal semantic grounding for LLM-based enterprise agents. We formalize the concept of asymmetric neurosymbolic coupling, wherein symbolic ontological knowledge constrains agent inputs (context assembly, tool discovery, governance thresholds) while proposing mechanisms for extending this coupling to constrain agent outputs (response validation, reasoning verification, compliance checking). We evaluate the architecture through a controlled experiment (600 runs across five industries: FinTech, Insurance, Healthcare, Vietnamese Banking, and Vietnamese Insurance), finding that ontology-coupled agents significantly outperform ungrounded agents on Metric Accuracy (p < .001, W = .460), Regulatory Compliance (p = .003, W = .318), and Role Consistency (p < .001, W = .614), with improvements greatest where LLM parametric knowledge is weakest--particularly in Vietnam-localized domains. Our contributions include: (1) a formal three-layer enterprise ontology model, (2) a taxonomy of neurosymbolic coupling patterns, (3) ontology-constrained tool discovery via SQL-pushdown scoring, (4) a proposed framework for output-side ontological validation, (5) empirical evidence for the inverse parametric knowledge effect that ontological grounding value is inversely proportional to LLM training data coverage of the domain, and (6) a production system serving 21 industry verticals with 650+ agents.

Sivanantham Nallusamy, V. Vasanthi, T. Kavinkumar et al.

Optoelectronics is advancing sustainability and energy efficiency across various industries, including renewable energy, healthcare, and environmental monitoring. This review highlights the fundamentals and mechanisms behind key optoelectronic devices such as photovoltaics, light-emitting diodes (LEDs), and sensors. It explores how advancements in photovoltaic technologies, including silicon-based, thin-film, and perovskite solar cells, are improving solar energy conversion efficiency through innovations in light absorption, charge transport, and energy band engineering. In addition, the review covers energy-efficient lighting technologies, such as LEDs, organic light-emitting diodes (OLEDs), and quantum dot-based light-emitting diodes (QLEDs), and their growing role in improving lighting systems and display technologies. Emerging trends, including flexible and wearable optoelectronic devices and integration with the Internet of Things (IoT), are also discussed, with a focus on how they contribute to more sustainable and intelligent energy solutions. Challenges like technical limitations, environmental impact, and opportunities for future research are considered, emphasizing the need for continued innovation in materials and device design. Ultimately, this article outlines the critical role of optoelectronics in shaping a sustainable, energy-efficient future across global industries, from energy and healthcare to environmental management.

Md. Arif Hossain Chowdhury Anik, T. M. A. Iqbal Bin Belal, Mohammad Mazedul Islam et al.

Grid-scale energy storing technologies are critical for maintaining grid stability and managing intermittent renewable energy sources. They play a significant role in the transition to sustainable energy for future purposes. This review looks at recent innovations in various energy storage systems (ESSs). These include advanced batteries such as solid-state, flow, lithium–sulfur, and sodium-ion. These batteries improve energy density, safety, lifespan, and cost-effectiveness. The review also explores thermal energy storage technologies such as molten salt, phase change materials, and cryogenic systems. These technologies store and manage thermal energy efficiently. Mechanical storage methods, such as pumped hydro, compressed air, and flywheel systems, provide scalable, long-duration support. Hydrogen and power-to-gas technologies, including green hydrogen and synthetic methane, also offer a promising way to store surplus renewable electricity. These technologies convert excess energy into clean fuels, helping to decarbonize industries and transportation. Emerging gravity-based storage systems and supercapacitor-hybrid technologies are also addressing storage challenges related to intermittency. They provide high-efficiency, long-duration storage. Virtual power plants (VPPs), blockchain for distributed energy exchange, and artificial intelligence-driven optimization are among the recently developed software technologies, which are simplifying ESSs. These modern technologies facilitate the addition of energy storage devices into the grid. Still, certain issues demand attention. The start-up expenses, desire for environmentally friendly products, and restrictions one must follow make it still difficult. It is still challenging because of the high start-up costs, demand for ecologically friendly items, and rules one must follow. This study underlines the importance of continually producing new ideas and of having policies supporting them. These projects will help to acquire energy storage devices for growing populations of people. They will help build a more dependable, reasonably priced, and long-lasting energy infrastructure over time.

Alswat Haitham M.

Incoloy alloy 800, a type of superalloy, is well-suited for industries that require high corrosion resistance. Laser beam welding (LBW) is an effective method for improving the quality of its joints. In this study, Incoloy alloy 800 is joined using Nd:YAG LBW by varying laser power between 2 and 3 kW with a constant welding speed of 2 m·min−1. Joints were analyzed using microscopic and mechanical testing. The observed weld zone has an hourglass shape and elongated columnar structure as well as dendrites with fine equiaxed grains. Remarkable phase changes occur due to the high cooling rate, which is associated with LBW. The reduction in mechanical properties was observed at high laser power due to the laves formation. The mode of fracture was changed from ductile to brittle while increasing the laser power.

Varun Raaghav, Dimitrios Bikos, Antonio Rago et al.

Composites are amongst the most important materials manufactured today, as evidenced by their use in countless applications. In order to establish the suitability of composites in specific applications, finite element (FE) modelling, a numerical method based on partial differential equations, is the industry standard for assessing their mechanical properties. However, FE modelling is exceptionally costly from a computational viewpoint, a limitation which has led to efforts towards applying AI models to this task. However, in these approaches: the chosen model architectures were rudimentary, feed-forward neural networks giving limited accuracy; the studies focused on predicting elastic mechanical properties, without considering material strength limits; and the models lacked transparency, hindering trustworthiness by users. In this paper, we show that convolutional neural networks (CNNs) equipped with methods from explainable AI (XAI) can be successfully deployed to solve this problem. Our approach uses customised CNNs trained on a dataset we generate using transverse tension tests in FE modelling to predict composites' mechanical properties, i.e., Young's modulus and yield strength. We show empirically that our approach achieves high accuracy, outperforming a baseline, ResNet-34, in estimating the mechanical properties. We then use SHAP and Integrated Gradients, two post-hoc XAI methods, to explain the predictions, showing that the CNNs use the critical geometrical features that influence the composites' behaviour, thus allowing engineers to verify that the models are trustworthy by representing the science of composites.

M. Denaige McDonnell, Branford J. McAllister

Angelika Zaszczyńska, A. Gradys, P. Sajkiewicz

Smart piezoelectric materials are of great interest due to their unique properties. Piezoelectric materials can transform mechanical energy into electricity and vice versa. There are mono and polycrystals (piezoceramics), polymers, and composites in the group of piezoelectric materials. Recent years show progress in the applications of piezoelectric materials in biomedical devices due to their biocompatibility and biodegradability. Medical devices such as actuators and sensors, energy harvesting devices, and active scaffolds for neural tissue engineering are continually explored. Sensors and actuators from piezoelectric materials can convert flow rate, pressure, etc., to generate energy or consume it. This paper consists of using smart materials to design medical devices and provide a greater understanding of the piezoelectric effect in the medical industry presently. A greater understanding of piezoelectricity is necessary regarding the future development and industry challenges.

Zixing Gao, Erman Xia, Sirui Lin et al.

Abstract In the context of China's resolute advancement of dual carbon goals (carbon peaking and carbon neutrality), urban agglomerations emerge as pivotal areas for carbon emission mitigation due to their dense economic activities and rapid urbanization. Previous studies overlook regional disparities in carbon emission prediction, disregarding the variations and policy directives across different provinces or cities. Therefore, this study addresses the research gap by investigating synergistic strategies to foster regional carbon peaking within the Beijing-Tianjin-Hebei region. Employing a novel approach tailored to regional segmentation policies, we provide more accurate predictions reflecting real-world conditions and distinct policy landscapes. Meanwhile, we integrate carbon emission efficiency into our analysis, emphasizing the dual goals of emission reduction and quality economic growth. Our empirical investigation in the Beijing-Tianjin-Hebei region, utilizing the Super-SBM and extended STIRPAT models, reveals upward trends in carbon emission efficiency, with varying trajectories across cities. Scenario simulations informed by the "14th Five-Year Plan" demonstrate that under the green development scenario, carbon peaking accelerates, alongside enhanced efficiency, supporting long-term emission reduction. Moreover, we design seven regional synergy carbon peak strategies for scenario simulations to facilitate the rational layout of dual carbon policies for collaborative development. We find that synergistic strategies have proven more effective in reducing regional carbon emission and increasing efficiency than strategies focusing solely on economic development or energy conservation. This innovative finding emphasizes the necessity of comprehensive green development in the Beijing-Tianjin-Hebei region and provides strong evidence for policymakers. Our research contributes to targeted strategies for improving carbon emission efficiency and reducing emissions, emphasizing the importance of synergistic approaches for regional carbon reduction.

Chen Li, Ruijie Ma, Xiang Qian et al.

Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm. This work introduces Style Filter, a tailored methodology for industrial contexts. By selectively filtering source domain data before knowledge transfer, Style Filter reduces the quantity of data while maintaining or even enhancing the performance of transfer learning strategy. Offering label-free operation, minimal reliance on prior knowledge, independence from specific models, and re-utilization, Style Filter is evaluated on authentic industrial datasets, highlighting its effectiveness when employed before conventional transfer strategies in the deep learning domain. The results underscore the effectiveness of Style Filter in real-world industrial applications.

Christian W. Frey

Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch

Xiaomeng Zhu, Talha Bilal, Pär Mårtensson et al.

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset and code are publicly available.